Abstract Somatic copy number alterations (SCNAs) are a type of mutation in cancer that affect more of the cancer genome than any other genetic event. SCNAs often contribute to cancer development and progression, and detecting them can contribute to the development of diagnostic and therapeutic advances in clinical care. As part of The Cancer Genome Atlas (TCGA) project our group characterized SCNAs for over 10,000 tumors across 30 different tumor types. Through these efforts we developed state-of-the-art methods to detect and interpret SCNAs, and used these to discover SCNAs that recur across many tumors and likely contribute to the formation of these tumors, the candidate tumor suppressors and oncogenes these SCNAs target, and novel clinically relevant SCNA-based cancer subtypes. We have also developed methods to detect SCNAs and the rearrangements that bound them from high-throughput sequencing data of the type being collected by the Genomics Data Analysis Network (GDAN). These methods resolve SCNAs, the mechanisms by which they arise, and their potential biological consequences, in much greater detail than could be done with microarray data generated for TCGA. Leveraging our experience in SCNA analysis, we propose to establish a Genomics Data Analysis Center (GDAC) that will service the GDAN with comprehensive, advanced analyses of SCNAs and the rearrangements that bound them, with the goals of identifying biologically and clinically relevant patterns of SCNA and disseminating this information to the GDAN and wider research community. We will: 1) Generate basic and quality control information for each tumor. We determine the fraction of cancer cells within each tumor (tumor purity) and the average copy number genomewide (ploidy). We will also test every putative pair of tumor and normal DNA samples to ensure that they did originate in the same person. 2) Characterize SCNAs and rearrangements in each tumor, including clonal and subclonal amplifications, deletions, loss of heterozygosity, and complex events like chromothripsis, firestorms, and isochromosomes. 3) Identify recurrent SCNAs and rearrangements that are likely to drive tumor development and progression, and the oncogenes and tumor suppressor genes they likely target. 4) Classify tumors by previously identified SCNA subtypes and discover new subtypes. We will identify SCNAs and genomewide patterns of SCNA that correlate with clinical and molecular features of tumors. 5) Integrate with the GDAN and research community. We will integrate our analytic pipelines with those of other GDACs; immerse ourselves in cooperative Analysis Working Groups formed by the GDAN to refine those analyses in light of the most important questions; make our analysis results available to other members of the GDAN in real time; and disseminate those results to the wider research community through our existing web portal and by working closely with other GDACs to integrate our analyses into their web portals. Our results will inform how SCNAs cause cancer and indicate new diagnostic and therapeutic strategies.